IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v483y2017icp171-181.html
   My bibliography  Save this article

Hybrid attacks on model-based social recommender systems

Author

Listed:
  • Yu, Junliang
  • Gao, Min
  • Rong, Wenge
  • Li, Wentao
  • Xiong, Qingyu
  • Wen, Junhao

Abstract

With the growing popularity of the online social platform, the social network based approaches to recommendation emerged. However, because of the open nature of rating systems and social networks, the social recommender systems are susceptible to malicious attacks. In this paper, we present a certain novel attack, which inherits characteristics of the rating attack and the relation attack, and term it hybrid attack. Furtherly, we explore the impact of the hybrid attack on model-based social recommender systems in multiple aspects. The experimental results show that, the hybrid attack is more destructive than the rating attack in most cases. In addition, users and items with fewer ratings will be influenced more when attacked. Last but not the least, the findings suggest that spammers do not depend on the feedback links from normal users to become more powerful, the unilateral links can make the hybrid attack effective enough. Since unilateral links are much cheaper, the hybrid attack will be a great threat to model-based social recommender systems.

Suggested Citation

  • Yu, Junliang & Gao, Min & Rong, Wenge & Li, Wentao & Xiong, Qingyu & Wen, Junhao, 2017. "Hybrid attacks on model-based social recommender systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 171-181.
    • Handle: RePEc:eee:phsmap:v:483:y:2017:i:c:p:171-181
    DOI: 10.1016/j.physa.2017.04.048
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437117303436
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2017.04.048?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Qian, Fulan & Zhao, Shu & Tang, Jie & Zhang, Yanping, 2016. "SoRS: Social recommendation using global rating reputation and local rating similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 61-72.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jasem M. Alostad, 2019. "Improving the Shilling Attack Detection in Recommender Systems Using an SVM Gaussian Mixture Model," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 1-18, March.
    2. Shanshan Wan & Ying Liu, 2022. "A security detection approach based on autonomy-oriented user sensor in social recommendation network," International Journal of Distributed Sensor Networks, , vol. 18(3), pages 15501329221, March.
    3. Tauli-Corpuz, Vicky & Alcorn, Janis & Molnar, Augusta & Healy, Christina & Barrow, Edmund, 2020. "Cornered by PAs: Adopting rights-based approaches to enable cost-effective conservation and climate action," World Development, Elsevier, vol. 130(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Deng, Xiuqin & Liu, Taiheng & Li, Wenzhou & Liu, Fuchun & Peng, Jiaen, 2019. "A latent factor model of fusing social regularization term and item regularization term," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1330-1342.
    2. Latha, R., 2022. "Enhancing recommendation competence in nearest neighbour models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:483:y:2017:i:c:p:171-181. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.